Technical Guide
14 min read
SwiftInference: Consistent Low-Latency AI Inference at the Network Edge
We present SwiftInference, a distributed edge AI inference platform achieving consistent sub-125ms P90 latency through strategic GPU placement at telecommunications sites. Across 1000 trials under mobile-realistic WiFi conditions, edge deployment demonstrates 36% faster P90 latency (125ms vs 194ms) and 4x lower variance (σ=27ms vs σ=100ms) compared to cloud infrastructure, despite using GPU hardware with 3x slower raw compute performance. While cloud providers achieve 70ms median latency through aggressive caching, this optimization creates bimodal behavior with high variance—50% of requests experience 180-256ms latency. Edge placement delivers unimodal consistency with 111ms median and tight 27ms standard deviation, enabling strict P99 SLA guarantees (<185ms) that cloud providers cannot economically match. Our architecture separates control and data planes, enabling towers to remain inbound-dark for management while accepting inference traffic via carrier on-net paths. For SLA-driven workloads requiring predictable latency—autonomous vehicles, real-time voice AI, industrial robotics—variance reduction and tail latency optimization represent more valuable metrics than median speed. Production deployment with matching GPU hardware (RTX PRO 6000 Blackwell) projects 60% end-to-end latency advantage while maintaining architectural variance benefits, positioning edge inference as both faster and more consistent than cloud alternatives.
Technical Guide
5 min read
Build a Document Q&A Pipeline with Open-Weights Embeddings
Learn how to build a production-ready document Q&A pipeline using open-weights embedding models, vector search, and a retrieval-augmented generation pattern. This hands-on tutorial walks you through every step from chunking raw documents to serving accurate answers.
Technical Guide
5 min read
Deploy a Scalable AI Chat API With Streaming Responses
Learn how to build and deploy a production-ready AI chat API that streams responses to clients using FastAPI, server-sent events, and a lightweight cloud setup. This tutorial walks you through every step, from local dev to a live endpoint that handles real traffic.
Technical Guide
5 min read
Build an AI Content Moderation Pipeline with Open-Source Models
Learn how to wire together open-source classifiers and LLMs into a production-ready content moderation pipeline that catches harmful text, images, and edge cases. This hands-on guide walks you through every step, from model selection to deployment considerations.
Technical Guide
5 min read
Build a Document Q&A Pipeline With Open-Weights Embeddings
Learn how to build a fully local document Q&A system using open-weights embedding models, a vector store, and a retrieval-augmented generation pattern. This hands-on tutorial takes you from raw PDFs to accurate, cited answers in under an hour.
Technical Guide
5 min read
Model Quantisation: Cut Inference Costs Without Losing Quality
Model quantisation can slash your inference costs by up to 4x while preserving most of your model's accuracy. This hands-on tutorial walks you through INT8 and INT4 quantisation using Hugging Face and bitsandbytes, covering real pitfalls and how to sidestep them.
Technical Guide
5 min read
Run LLM Inference on CPU with llama.cpp and a REST API
Learn how to compile llama.cpp, load a quantized model, and expose it through a local REST API endpoint — all without a GPU. A practical walkthrough for developers who need cost-effective, self-hosted language model inference.
Technical Guide
5 min read
Build a Low-Cost Semantic Search Engine With Open-Source Embeddings
Learn how to build a fully functional semantic search engine using free, open-source embedding models and a lightweight vector store — no expensive APIs required. This hands-on tutorial walks you through every step, from encoding documents to querying results in milliseconds.
Technical Guide
4 min read
Run LLM Inference on CPU With llama.cpp and a REST API
Learn how to compile llama.cpp, download a quantized model, and expose it through a local REST API — all without a GPU. This tutorial walks you through every step so you can run production-grade language model inference on any Linux or macOS machine.